B.Sc. Statistics and Data Analytics
Introduction to the Program:
This course has been frame out on the based on fundamental of Statistics and its use in data analytics with the aim that student under this course enriched by the core of modern data analytics that turn data into intelligence to inform decision-making and solve challenging problems. Applications range from economics and medicine, to social and environmental sciences. This degree covers theoretical and applied elements of modern statistics, and provides training and practical experience in modelling, analyzing and interpreting real data required in the economy, industry and research. The early years of the degree cover basic mathematics, probability and statistics. The final years focus on advanced specialist topics in statistical modelling, data science, machine learning, probability and stochastic processes.
Program Educational Objective:
PEO 01: Graduate will equip with latest techniques in Data Analytics like Python, Machine learning, Big Data etc.
PEO 02: Graduates will able to choose their course as a training ground to develop their positive attitude and skills.
PEO 03: Graduates of the program will become technically competent to pursue higher studies.
PEO 04: Graduates are prepared to survive in rapidly changing technology and engage in life-long learning.
PEO 05: Graduates will communicate effectively in both verbal and written form in industry and society.
Program Outcome:
PO 01: Academic Excellence: Understanding the academic field of Statistics and its different learning areas with applications.
PO 02: Contextualized Understanding: Develop the ability to distinguish between random and non-random experiments and simultaneously learn the theory and applications of probability
PO 03: Design/development of solutions: Identify, design and solve scientific problems based on data collection, data interpretation and analysis of results.
PO 04: Conduct investigations of complex problems: Explore various real-life problems and ways to solve them with a reliable solution using various statistical methods and tests.
PO 05: Quantitative Aspects: Learn to apply the tools of the various statistical and mathematical procedures with programming to solve real-life problems involving large data sets.
PO 06: Modernization and Tools Usage: Develop the ability in using modern statistical, mathematical and data analytics tools for design and analysis, and quality control.
PO 07: Societal Implication: Apply statistical methods and tools in societal, demographic, health, business and cultural issues
PO 08: Environment and Sustainability: Understand the tools towards problem solving and applications in biological science, agricultural science, and social sciences
PO 09: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of mathematical and data science.
PO 10: Individual and Team Work: Work effectively as an individual or as a member or leader in undertaking projects, research organizations, industries and multidisciplinary area
PO 11: Communication: Build up communication skills, both written and oral, so as to apply them to write effective reports.
PO 12: Life Long Learning: Develop the ability to evaluate theories, methods, principles, and applications of pure and applied Statistics and data science
Programme Specific Outcome:
PSO 01: Have the versatility to work effectively in a broad range of analytic, scientific, government, financial, health, technical and other positions.
PSO 02: Be familiar with a variety of examples where the knowledge of mathematics or statistics helps to explain the abstract or physical phenomena accurately.
PSO 03: Enhance theoretical rigor with technical skills which prepare students to become globally competitive to enter into a promising professional life in both government and private sector
Programme Eligibility:
Minimum 50% aggregate in 10 +2 or equivalent from any recognized board with Mathematics/Statistics as one of the subject.
Duration (in Year): 4 (with exit option after 3 years as per NEP guidelines).
Career Avenues:
- There is scope in Banking, Finance institutions, R&D firms, Actuarial Science etc.
- Federal bank, Cognizant, IBM, Infosys, Wipro, Deloitte, HDFC Bank, Google, etc. are some of top recruiters of the field.
- A candidate may also pursue bureaucrat jobs like ISS, IES and IAS.
- There is demand in urban planning companies of both government and private types.
- This course plays a huge role in getting through the fields of risk assessment and management.
- A candidate may also pursue for higher degrees and opt for teaching jobs in colleges or universities.
LIST OF ‘DISCIPLINE SPECIFIC ELECTIVE PAPERS (DSE)’ OFFERED BY THE DEPT. OF MATHEMATICS:
|
List of Elective Papers |
||||
|
Electives |
Paper Name |
Paper Code |
Credit |
L-T-P |
|
DSE I |
ECONOMETRICS |
ECO11504 |
4 |
3-1-0 |
|
STATISTICAL QUALITY CONTROL |
SDS11086 |
4 |
3-1-0 |
|
|
SOFT COMPUTING |
MTH11038 |
4 |
3-1-0 |
|
|
DESIGN AND ANALYSIS OF ALGORITHM |
CSE11659 |
4 |
3-1-0 |
|
|
DSE II
|
DEMOGRAPHY AND SURVIVAL ANALYSIS |
SDS11087 |
4 |
3-1-0 |
|
INTRODUCTION TO BIG DATA |
CSE11652 |
4 |
3-1-0 |
|
|
INTRODUCTION TO FINANCIAL RISK ANALYTICS |
SDS11088 |
4 |
3-1-0 |
|
|
ACTUARIAL STATISTICS |
SDS11037 |
4 |
3-1-0 |
|
|
DSE III
|
INTRODUCTION TO DEEP LEARNING |
CSE11653 |
4 |
3-1-0 |
|
INTRODUCTION TO DEEP LEARNING PRACTICAL |
CSE12654 |
2 |
0-0-3 |
|
|
DATA MANIPULATION AND DATA CLEANING IN R |
SDS11030 |
4 |
3-1-0 |
|
|
DATA MANIPULATION AND DATA CLEANING IN R PRACTICAL |
SDS12031 |
2 |
0-0-3 |
|
|
SURVEY SAMPLING |
SDS11089 |
4 |
3-1-0 |
|
|
SURVEY SAMPLING PRACTICAL |
SDS12090 |
2 |
0-0-3 |
|
|
INTRODUCTION TO NUMERICAL ANALYSIS |
MTH11017 |
4 |
3-1-0 |
|
|
NUMERICAL ANALYSIS LAB |
MTH12019 |
2 |
0-0-3 |
|
